
import pandas as pd
#import data set
#view data
diabetes = pd.read_csv(r'I:\diabetes\classified数据\train_classified.csv')
diabetes.info()
print(diabetes.describe())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5641 entries, 0 to 5640
Data columns (total 40 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    5641 non-null   int64  
 1   age                    5641 non-null   int64  
 2   AST                    4421 non-null   float64
 3   ALT                    4421 non-null   float64
 4   ALP                    4421 non-null   float64
 5   GGT                    4421 non-null   float64
 6   TP                     4421 non-null   float64
 7   Alb                    4421 non-null   float64
 8   GLB                    4421 non-null   float64
 9   A/G                    4421 non-null   float64
 10  TG                     4423 non-null   float64
 11  TCHO                   4423 non-null   float64
 12  HDL-C                  4423 non-null   float64
 13  LDL-C                  4423 non-null   float64
 14  Urea                   4263 non-null   float64
 15  Cre                    4263 non-null   float64
 16  UA                     4263 non-null   float64
 17  HBsAg                  1363 non-null   float64
 18  Second liver antibody  1363 non-null   float64
 19  HBeAg                  1363 non-null   float64
 20  HBeAb                  1363 non-null   float64
 21  HBcAb                  1363 non-null   float64
 22  WBC                    5625 non-null   float64
 23  RBC                    5625 non-null   float64
 24  HGB                    5625 non-null   float64
 25  PCV                    5625 non-null   float64
 26  MCV                    5625 non-null   float64
 27  MCH                    5625 non-null   float64
 28  MCHC                   5625 non-null   float64
 29  RDW                    5625 non-null   float64
 30  PLT                    5625 non-null   float64
 31  MPV                    5618 non-null   float64
 32  PDW                    5618 non-null   float64
 33  PCT                    5618 non-null   float64
 34  NEUT%                  5625 non-null   float64
 35  LY%                    5625 non-null   float64
 36  MONO%                  5625 non-null   float64
 37  EOS%                   5625 non-null   float64
 38  BAS%                   5625 non-null   float64
 39  GLU                    5641 non-null   int64  
dtypes: float64(37), int64(3)
memory usage: 1.7 MB
               sex          age          AST         ALT          ALP  \
count  5641.000000  5641.000000  4421.000000  4421.00000  4421.000000   
mean      0.489984    45.581280    26.805892    27.65202    87.482278   
std       0.499944    12.962952    13.563003    22.61152    25.630897   
min       0.000000     3.000000    10.040000     0.12000    22.980000   
25%       0.000000    35.000000    20.250000    15.13000    70.370000   
50%       0.000000    45.000000    23.890000    21.48000    84.470000   
75%       1.000000    54.000000    29.270000    32.30000   100.210000   
max       1.000000    93.000000   434.950000   498.89000   374.320000   

               GGT           TP          Alb          GLB          A/G  \
count  4421.000000  4421.000000  4421.000000  4421.000000  4421.000000   
mean     38.823762    76.771108    45.822504    30.948604     1.502538   
std      40.788282     4.016055     2.617401     3.565997     0.220621   
min       6.360000    57.320000    29.540000     7.060000     0.520000   
25%      17.810000    74.190000    44.130000    28.570000     1.360000   
50%      26.190000    76.630000    45.820000    30.780000     1.490000   
75%      43.850000    79.530000    47.570000    33.180000     1.630000   
max     736.990000   100.410000    54.080000    66.180000     7.120000   

                TG         TCHO        HDL-C        LDL-C         Urea  \
count  4423.000000  4423.000000  4423.000000  4423.000000  4263.000000   
mean      1.846005     5.230631     1.390036     3.362935     4.985050   
std       1.780796     1.027232     0.316718     0.860141     1.301019   
min       0.270000     1.850000     0.540000     0.560000     1.500000   
25%       0.970000     4.530000     1.170000     2.760000     4.090000   
50%       1.440000     5.150000     1.350000     3.310000     4.870000   
75%       2.165000     5.820000     1.570000     3.900000     5.720000   
max      41.570000    20.460000     5.280000     8.460000    13.390000   

               Cre           UA        HBsAg  Second liver antibody  \
count  4263.000000  4263.000000  1363.000000            1363.000000   
mean     78.460680   355.651274     0.950393               7.005806   
std      13.842182    95.952683     5.699399               8.206147   
min      39.430000   118.670000     0.000000               0.000000   
25%      67.780000   285.030000     0.010000               0.225000   
50%      77.880000   347.580000     0.050000               3.290000   
75%      87.510000   415.200000     0.090000              12.195000   
max     177.420000   774.060000    44.350000              42.490000   

             HBeAg        HBeAb        HBcAb          WBC          RBC  \
count  1363.000000  1363.000000  1363.000000  5625.000000  5625.000000   
mean      0.073224     1.751340     1.886628     6.589179     4.957003   
std       0.536642     0.819257     1.579841     1.602795     0.503177   
min       0.010000     0.000000     0.000000     2.800000     3.010000   
25%       0.010000     1.280000     1.100000     5.470000     4.580000   
50%       0.040000     1.630000     1.640000     6.380000     4.930000   
75%       0.080000     2.090000     2.170000     7.460000     5.320000   
max      17.520000     7.170000    17.090000    21.060000     6.850000   

               HGB          PCV          MCV          MCH         MCHC  \
count  5625.000000  5625.000000  5625.000000  5625.000000  5625.000000   
mean    147.977902     0.440733    89.049493    29.883396   335.415289   
std      16.546632     0.043252     4.459055     1.995134    11.390254   
min      65.000000     0.239000    59.000000    16.000000   270.000000   
25%     136.000000     0.410000    86.900000    29.000000   329.000000   
50%     148.000000     0.440000    89.200000    30.000000   336.000000   
75%     161.000000     0.473000    91.700000    31.000000   342.000000   
max     204.000000     0.599000   113.000000    44.700000   462.000000   

               RDW          PLT          MPV          PDW          PCT  \
count  5625.000000  5625.000000  5618.000000  5618.000000  5618.000000   
mean     12.737707   253.082667    10.653346    13.304254     0.267901   
std       1.001387    59.591793     0.985545     2.173877     0.060507   
min      10.900000    37.000000     7.100000     8.000000     0.042000   
25%      12.200000   213.000000    10.000000    11.700000     0.230000   
50%      12.600000   249.000000    10.600000    12.900000     0.260000   
75%      13.000000   289.000000    11.300000    14.600000     0.300000   
max      23.800000   745.000000    15.200000    25.300000     0.710000   

             NEUT%          LY%        MONO%         EOS%         BAS%  \
count  5625.000000  5625.000000  5625.000000  5625.000000  5625.000000   
mean     56.717404    33.779218     6.856978     2.043982     0.603698   
std       7.800225     7.253566     1.567647     1.710997     0.291786   
min      14.400000     7.500000     3.100000     0.000000     0.000000   
25%      51.600000    28.800000     5.800000     0.900000     0.400000   
50%      56.700000    33.600000     6.700000     1.600000     0.600000   
75%      62.000000    38.500000     7.700000     2.600000     0.700000   
max      88.500000    76.300000    23.200000    22.500000     3.500000   

               GLU  
count  5641.000000  
mean      0.321397  
std       0.467054  
min       0.000000  
25%       0.000000  
50%       0.000000  
75%       1.000000  
max       1.000000


pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
x = diabetes.drop("GLU", axis = 1)
x


y = diabetes[["GLU"]]
y


# transform values of glucose into 0 or 1
# pd.set_option('display.max_rows', None)
# y["GLU"][y.GLU<5.6]= 0
# y["GLU"][y.GLU>=5.6]= 1
# y
# replace results below into answers of test

C:\Users\Xzt\AppData\Local\Temp\ipykernel_9716\689249385.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  y["GLU"][y.GLU<5.6]= 0
C:\Users\Xzt\AppData\Local\Temp\ipykernel_9716\689249385.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  y["GLU"][y.GLU>=5.6]= 1


# interpolation
trains_cla = diabetes.interpolate()
trains_cla = trains_cla.fillna(trains_cla.mean())
trains_cla


trains_cla.info()
print(len(trains_cla))

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5641 entries, 0 to 5640
Data columns (total 40 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    5641 non-null   int64  
 1   age                    5641 non-null   int64  
 2   AST                    5641 non-null   float64
 3   ALT                    5641 non-null   float64
 4   ALP                    5641 non-null   float64
 5   GGT                    5641 non-null   float64
 6   TP                     5641 non-null   float64
 7   Alb                    5641 non-null   float64
 8   GLB                    5641 non-null   float64
 9   A/G                    5641 non-null   float64
 10  TG                     5641 non-null   float64
 11  TCHO                   5641 non-null   float64
 12  HDL-C                  5641 non-null   float64
 13  LDL-C                  5641 non-null   float64
 14  Urea                   5641 non-null   float64
 15  Cre                    5641 non-null   float64
 16  UA                     5641 non-null   float64
 17  HBsAg                  5641 non-null   float64
 18  Second liver antibody  5641 non-null   float64
 19  HBeAg                  5641 non-null   float64
 20  HBeAb                  5641 non-null   float64
 21  HBcAb                  5641 non-null   float64
 22  WBC                    5641 non-null   float64
 23  RBC                    5641 non-null   float64
 24  HGB                    5641 non-null   float64
 25  PCV                    5641 non-null   float64
 26  MCV                    5641 non-null   float64
 27  MCH                    5641 non-null   float64
 28  MCHC                   5641 non-null   float64
 29  RDW                    5641 non-null   float64
 30  PLT                    5641 non-null   float64
 31  MPV                    5641 non-null   float64
 32  PDW                    5641 non-null   float64
 33  PCT                    5641 non-null   float64
 34  NEUT%                  5641 non-null   float64
 35  LY%                    5641 non-null   float64
 36  MONO%                  5641 non-null   float64
 37  EOS%                   5641 non-null   float64
 38  BAS%                   5641 non-null   float64
 39  GLU                    5641 non-null   int64  
dtypes: float64(37), int64(3)
memory usage: 1.7 MB
5641


# pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', None)
x = trains_cla.drop("GLU", axis = 1)
x


y = trains_cla[["GLU"]]
y


# Histogram
import matplotlib.pyplot as plt
import seaborn as sns
import hvplot.pandas
%matplotlib inline

# Retina display
%config InlineBackend.figure_format = 'retina'

# plt.style.use('ggplot') 
plt.style.use("fivethirtyeight")

# Pandas Display three decimal 
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))

for k in ['AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea','Cre','UA','HBsAg',
          'Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV','MCH','MCHC','RDW','PLT','MPV','PDW',
          'PCT','NEUT%','LY%','MONO%','EOS%','BAS%']:
    plt.figure(figsize = (15,5))
    sns.barplot(x = trains_cla[k]. value_counts().index, y = trains_cla[k].value_counts().values)
    plt.title(k)

C:\Users\Xzt\AppData\Local\Temp\ipykernel_9716\1334747885.py:19: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  plt.figure(figsize = (15,5))


corr = trains_cla.corr()
plt.figure(figsize = (25, 20))

sns.heatmap(corr,
           annot = True,
           fmt = ".1f",
#            cmap = "YlGn" 
           )
plt.show()


corr["GLU"].sort_values()

sex                     -0.181
PCT                     -0.083
RDW                     -0.080
PLT                     -0.073
HDL-C                   -0.048
Second liver antibody   -0.040
LY%                     -0.030
MPV                     -0.023
HBeAb                   -0.018
HBeAg                   -0.007
A/G                     -0.003
PDW                     -0.001
HBcAb                    0.010
MONO%                    0.013
HBsAg                    0.015
NEUT%                    0.017
GLB                      0.025
Alb                      0.030
EOS%                     0.032
BAS%                     0.033
TP                       0.042
MCV                      0.046
WBC                      0.070
UA                       0.088
ALP                      0.095
MCH                      0.100
AST                      0.105
ALT                      0.113
RBC                      0.122
Cre                      0.127
Urea                     0.133
MCHC                     0.134
GGT                      0.137
PCV                      0.149
TCHO                     0.150
LDL-C                    0.164
HGB                      0.169
TG                       0.169
age                      0.294
GLU                      1.000
Name: GLU, dtype: float64


sns.pairplot(trains_cla[["age","sex","TG","GLU"]]) # The first three characteristics of absolute value
plt.show()


# import test
testA_cla = pd.read_csv(r'I:\diabetes\classified数据\testA_classified.csv')
testA_cla.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 39 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    1000 non-null   int64  
 1   age                    1000 non-null   int64  
 2   AST                    815 non-null    float64
 3   ALT                    815 non-null    float64
 4   ALP                    815 non-null    float64
 5   GGT                    815 non-null    float64
 6   TP                     815 non-null    float64
 7   Alb                    815 non-null    float64
 8   GLB                    815 non-null    float64
 9   A/G                    815 non-null    float64
 10  TG                     824 non-null    float64
 11  TCHO                   824 non-null    float64
 12  HDL-C                  824 non-null    float64
 13  LDL-C                  824 non-null    float64
 14  Urea                   806 non-null    float64
 15  Cre                    806 non-null    float64
 16  UA                     806 non-null    float64
 17  HBsAg                  169 non-null    float64
 18  Second liver antibody  169 non-null    float64
 19  HBeAg                  169 non-null    float64
 20  HBeAb                  169 non-null    float64
 21  HBcAb                  169 non-null    float64
 22  WBC                    995 non-null    float64
 23  RBC                    995 non-null    float64
 24  HGB                    995 non-null    float64
 25  PCV                    995 non-null    float64
 26  MCV                    995 non-null    float64
 27  MCH                    995 non-null    float64
 28  MCHC                   995 non-null    float64
 29  RDW                    995 non-null    float64
 30  PLT                    995 non-null    float64
 31  MPV                    994 non-null    float64
 32  PDW                    994 non-null    float64
 33  PCT                    994 non-null    float64
 34  NEUT%                  995 non-null    float64
 35  LY%                    995 non-null    float64
 36  MONO%                  995 non-null    float64
 37  EOS%                   995 non-null    float64
 38  BAS%                   995 non-null    float64
dtypes: float64(37), int64(2)
memory usage: 304.8 KB


# interpolation of test
testA_cla_1 = testA_cla.interpolate()
testA_cla_1 = testA_cla_1.fillna(testA_cla_1.mean())
testA_cla_1


# import test answer
testA_cla_answer = pd.read_csv(r'I:\diabetes\classified数据\answerA_classified.csv')
testA_cla_answer.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   GLU     1000 non-null   int64
dtypes: int64(1)
memory usage: 7.9 KB


# y_cla = test_cla_answer

# y_cla["GLU"][y_cla.GLU<5.6]=0
# y_cla["GLU"][y_cla.GLU>=5.6]=1
# y_cla
testA_cla_answer


# neural network
# sklearn learn neural network

from sklearn.neural_network import MLPClassifier
from sklearn import metrics

# Model instantiation
mlp = MLPClassifier(solver = 'lbfgs',alpha = 1e-5, hidden_layer_sizes = (10, 5), max_iter = 3000)
mlp.fit(x, y.values.ravel())
prediction = mlp.predict(testA_cla_1)

#accuracy
print('The accuracy of the neural network is: {0}'.format(metrics.accuracy_score(prediction, testA_cla_answer)))

The accuracy of the neural network is: 0.66


# decision tree
# Establish decision tree model based on information entropy

from sklearn.tree import DecisionTreeClassifier as DTC
from sklearn.tree import export_graphviz

dtc = DTC(criterion = 'entropy')
# train model
dtc.fit(x, y.values.ravel())
prediction = dtc.predict(testA_cla_1)
# accuracy
print('The accuracy of the neural network is: {0}'.format(metrics.accuracy_score(prediction, testA_cla_answer)))

The accuracy of the neural network is: 0.601


# Gridsearch to find optimal parameters to optimize the model

from sklearn.model_selection import GridSearchCV

gini_thresholds = np.linspace(0, 0.5, 20)
parameters = {
    'splitter':('best', 'random')
    ,'criterion':("gini", "entropy")
    ,"max_depth":[*range(1, 10)]
    ,'min_samples_leaf':[*range(1, 50, 5)]
    ,'min_impurity_decrease':[*np.linspace(0, 0.5, 20)]
}
clf = DTC(random_state = 25)
GS = GridSearchCV(clf, parameters, cv = 10, scoring = 'roc_auc')
GS.fit(x, y.values.ravel())

print(GS.best_score_)
print(GS.best_params_)

0.6999260259785729
{'criterion': 'gini', 'max_depth': 5, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 41, 'splitter': 'random'}


# optimized parameters are substituted into the reconstructed model

dtc_new = DTC(
criterion = 'gini',
max_depth = 5,
min_impurity_decrease = 0.0,
min_samples_leaf = 41,
splitter = 'random'
)

dtc_new.fit(x, y.values.ravel())
prediction = dtc_new.predict(testA_cla_1)
print('The accuracy of the neural network is: {0}'.format(metrics.accuracy_score(prediction, testA_cla_answer)))

The accuracy of the neural network is: 0.668

	GLU
0	0
1	0
2	0
3	1
4	1
5	0
6	0
7	1
8	0
9	1
10	0
11	0
12	0
13	0
14	1
15	1
16	0
17	1
18	1
19	1
20	0
21	1
22	1
23	1
24	0
25	0
26	0
27	1
28	1
29	0
30	0
31	0
32	0
33	1
34	0
35	1
36	1
37	0
38	0
39	0
40	0
41	1
42	0
43	1
44	0
45	1
46	1
47	0
48	0
49	1
50	0
51	1
52	0
53	0
54	0
55	0
56	0
57	0
58	0
59	0
60	0
61	0
62	1
63	0
64	0
65	0
66	0
67	0
68	0
69	0
70	1
71	1
72	0
73	0
74	0
75	0
76	0
77	0
78	1
79	1
80	1
81	0
82	1
83	0
84	0
85	0
86	0
87	0
88	1
89	1
90	0
91	1
92	1
93	1
94	0
95	0
96	1
97	1
98	1
99	1
100	0
101	1
102	1
103	1
104	1
105	0
106	0
107	0
108	0
109	1
110	1
111	0
112	1
113	1
114	0
115	1
116	0
117	0
118	1
119	0
120	1
121	0
122	0
123	0
124	0
125	1
126	0
127	0
128	0
129	0
130	0
131	0
132	0
133	1
134	0
135	0
136	0
137	1
138	1
139	1
140	0
141	0
142	1
143	1
144	0
145	0
146	1
147	0
148	1
149	0
150	1
151	0
152	0
153	0
154	0
155	0
156	0
157	1
158	1
159	0
160	0
161	1
162	1
163	0
164	1
165	1
166	0
167	0
168	0
169	0
170	1
171	0
172	0
173	1
174	0
175	1
176	0
177	0
178	0
179	0
180	1
181	0
182	0
183	0
184	0
185	0
186	0
187	1
188	0
189	0
190	0
191	0
192	1
193	0
194	0
195	0
196	0
197	1
198	1
199	1
200	0
201	1
202	0
203	1
204	0
205	0
206	1
207	1
208	1
209	0
210	0
211	0
212	0
213	0
214	1
215	0
216	1
217	1
218	1
219	0
220	1
221	0
222	0
223	0
224	0
225	1
226	0
227	1
228	1
229	0
230	0
231	0
232	0
233	1
234	0
235	1
236	0
237	1
238	0
239	0
240	1
241	1
242	0
243	1
244	1
245	1
246	1
247	1
248	0
249	1
250	1
251	0
252	1
253	1
254	1
255	0
256	0
257	1
258	1
259	0
260	1
261	0
262	1
263	0
264	0
265	1
266	1
267	1
268	1
269	0
270	0
271	1
272	1
273	0
274	0
275	1
276	1
277	0
278	0
279	0
280	0
281	1
282	1
283	1
284	1
285	0
286	1
287	0
288	0
289	0
290	1
291	1
292	1
293	1
294	1
295	0
296	1
297	0
298	1
299	1
300	1
301	0
302	1
303	1
304	0
305	0
306	0
307	0
308	1
309	0
310	1
311	0
312	0
313	1
314	0
315	1
316	1
317	1
318	1
319	0
320	0
321	0
322	0
323	0
324	0
325	1
326	0
327	1
328	0
329	0
330	0
331	0
332	1
333	0
334	0
335	1
336	1
337	1
338	0
339	1
340	0
341	1
342	1
343	0
344	1
345	0
346	0
347	0
348	0
349	0
350	0
351	0
352	0
353	1
354	0
355	0
356	1
357	0
358	1
359	1
360	0
361	1
362	0
363	0
364	0
365	0
366	1
367	0
368	1
369	0
370	1
371	0
372	1
373	0
374	0
375	0
376	1
377	0
378	1
379	0
380	0
381	1
382	0
383	1
384	1
385	0
386	1
387	1
388	0
389	0
390	0
391	1
392	1
393	0
394	0
395	1
396	1
397	1
398	1
399	0
400	0
401	0
402	0
403	1
404	0
405	0
406	0
407	0
408	1
409	0
410	1
411	0
412	0
413	1
414	0
415	1
416	0
417	0
418	0
419	1
420	1
421	1
422	1
423	0
424	0
425	0
426	0
427	0
428	0
429	1
430	0
431	1
432	0
433	0
434	0
435	1
436	1
437	0
438	0
439	0
440	1
441	0
442	1
443	0
444	0
445	0
446	1
447	0
448	0
449	0
450	0
451	1
452	1
453	0
454	1
455	1
456	1
457	0
458	1
459	1
460	1
461	0
462	0
463	0
464	1
465	0
466	1
467	0
468	0
469	0
470	0
471	0
472	0
473	0
474	0
475	0
476	1
477	0
478	0
479	0
480	1
481	0
482	1
483	0
484	0
485	1
486	0
487	0
488	0
489	0
490	0
491	0
492	0
493	0
494	1
495	0
496	0
497	0
498	0
499	0
500	0
501	0
502	0
503	1
504	0
505	1
506	0
507	0
508	0
509	0
510	1
511	1
512	0
513	0
514	0
515	0
516	0
517	1
518	0
519	0
520	0
521	0
522	1
523	1
524	1
525	0
526	0
527	0
528	0
529	0
530	0
531	0
532	0
533	0
534	1
535	0
536	0
537	0
538	0
539	0
540	0
541	0
542	1
543	0
544	1
545	1
546	0
547	1
548	0
549	1
550	1
551	0
552	0
553	0
554	0
555	0
556	1
557	0
558	1
559	0
560	1
561	1
562	0
563	0
564	1
565	1
566	0
567	1
568	1
569	1
570	0
571	1
572	0
573	0
574	1
575	0
576	1
577	0
578	0
579	1
580	0
581	0
582	1
583	0
584	1
585	0
586	0
587	1
588	1
589	1
590	0
591	0
592	0
593	1
594	1
595	0
596	0
597	1
598	1
599	1
600	1
601	1
602	1
603	0
604	0
605	1
606	0
607	1
608	1
609	1
610	0
611	0
612	1
613	1
614	1
615	1
616	0
617	0
618	0
619	1
620	0
621	0
622	0
623	0
624	0
625	0
626	0
627	0
628	1
629	1
630	0
631	0
632	0
633	0
634	1
635	1
636	1
637	1
638	0
639	1
640	1
641	1
642	0
643	1
644	0
645	0
646	0
647	1
648	1
649	0
650	0
651	0
652	0
653	0
654	1
655	0
656	0
657	0
658	1
659	0
660	0
661	0
662	0
663	0
664	0
665	0
666	0
667	1
668	1
669	1
670	0
671	0
672	1
673	0
674	0
675	0
676	0
677	0
678	0
679	0
680	1
681	0
682	0
683	1
684	1
685	1
686	0
687	0
688	0
689	0
690	1
691	1
692	0
693	0
694	1
695	0
696	0
697	0
698	1
699	1
700	0
701	0
702	0
703	0
704	0
705	0
706	0
707	1
708	0
709	1
710	0
711	0
712	0
713	1
714	1
715	0
716	0
717	0
718	0
719	0
720	0
721	1
722	1
723	0
724	1
725	0
726	1
727	0
728	0
729	0
730	0
731	0
732	1
733	0
734	0
735	0
736	0
737	0
738	0
739	0
740	1
741	0
742	0
743	0
744	0
745	1
746	0
747	1
748	1
749	1
750	1
751	1
752	0
753	0
754	1
755	0
756	1
757	0
758	0
759	0
760	0
761	0
762	0
763	1
764	0
765	0
766	0
767	0
768	0
769	1
770	0
771	0
772	0
773	1
774	0
775	0
776	0
777	1
778	1
779	0
780	0
781	0
782	1
783	0
784	0
785	0
786	0
787	0
788	1
789	1
790	0
791	1
792	0
793	1
794	0
795	0
796	0
797	1
798	0
799	0
800	0
801	0
802	0
803	0
804	0
805	0
806	1
807	0
808	0
809	0
810	0
811	0
812	0
813	1
814	1
815	0
816	1
817	0
818	1
819	0
820	0
821	1
822	1
823	1
824	0
825	1
826	0
827	1
828	0
829	1
830	1
831	0
832	1
833	0
834	0
835	1
836	0
837	0
838	0
839	1
840	0
841	1
842	0
843	0
844	1
845	1
846	1
847	0
848	0
849	1
850	1
851	0
852	0
853	1
854	0
855	1
856	0
857	0
858	0
859	1
860	0
861	0
862	1
863	1
864	0
865	0
866	0
867	0
868	1
869	0
870	1
871	1
872	0
873	0
874	0
875	0
876	0
877	0
878	0
879	1
880	0
881	1
882	1
883	0
884	0
885	1
886	0
887	1
888	0
889	0
890	0
891	0
892	1
893	0
894	0
895	0
896	0
897	1
898	0
899	1
900	1
901	0
902	0
903	0
904	1
905	0
906	1
907	0
908	0
909	0
910	0
911	0
912	0
913	1
914	1
915	1
916	1
917	0
918	0
919	0
920	0
921	1
922	0
923	0
924	1
925	1
926	0
927	0
928	1
929	1
930	1
931	1
932	0
933	1
934	1
935	0
936	1
937	0
938	1
939	0
940	0
941	0
942	0
943	1
944	0
945	1
946	0
947	0
948	0
949	0
950	0
951	1
952	1
953	0
954	1
955	0
956	0
957	0
958	0
959	1
960	0
961	0
962	0
963	0
964	0
965	0
966	0
967	0
968	0
969	0
970	1
971	0
972	0
973	1
974	0
975	0
976	0
977	0
978	0
979	0
980	1
981	0
982	0
983	1
984	0
985	1
986	0
987	1
988	1
989	0
990	0
991	0
992	0
993	1
994	1
995	0
996	0
997	0
998	0
999	1

	sex	age	AST	ALT	ALP	GGT	TP	Alb	GLB	A/G	TG	TCHO	HDL-C	LDL-C	Urea	Cre	UA	HBsAg	Second liver antibody	HBeAg	HBeAb	HBcAb	WBC	RBC	HGB	PCV	MCV	MCH	MCHC	RDW	PLT	MPV	PDW	PCT	NEUT%	LY%	MONO%	EOS%	BAS%
0	0	41	24.96	23.10	99.59	20.23	76.88	49.60	27.28	1.82	1.31	4.43	1.37	2.65	5.87	77.25	349.39	NaN	NaN	NaN	NaN	NaN	5.34	5.21	166.1	0.479	91.9	31.9	347.0	12.8	166.0	9.9	17.4	0.164	54.1	34.2	6.5	4.7	0.6
1	0	41	24.57	36.25	67.21	79.00	79.43	47.76	31.67	1.51	2.81	4.06	0.93	2.63	5.26	87.12	486.78	NaN	NaN	NaN	NaN	NaN	7.65	5.21	156.0	0.456	87.5	29.9	342.0	13.4	277.0	9.2	10.3	0.260	52.0	36.7	5.8	4.7	0.8
2	0	46	20.82	15.23	63.69	38.17	86.23	48.00	38.23	1.26	0.99	4.13	1.64	2.01	4.77	78.19	452.07	0.010	0.020	0.010	1.370	1.070	4.60	4.76	148.8	0.438	91.9	31.3	340.0	13.0	241.0	8.3	16.6	0.199	48.1	40.3	7.7	3.2	0.8
3	1	22	14.99	10.59	74.08	20.22	70.98	44.02	26.96	1.63	1.06	6.89	1.43	5.04	4.23	61.46	368.85	NaN	NaN	NaN	NaN	NaN	9.36	4.29	137.0	0.403	93.9	31.9	340.0	12.6	252.0	10.3	10.8	0.260	41.7	46.5	6.7	4.6	0.5
4	1	48	20.07	14.78	75.79	22.72	78.05	41.83	36.22	1.15	0.97	5.37	1.27	3.65	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.07	5.15	106.0	0.354	68.7	20.6	299.0	16.6	316.0	11.1	14.0	0.350	56.6	33.1	9.1	0.6	0.6
5	1	74	23.72	22.59	81.23	23.35	76.46	45.85	30.61	1.50	2.45	6.65	1.81	4.28	6.11	66.66	383.87	0.050	0.550	0.010	1.220	1.570	7.00	4.64	141.3	0.413	88.8	30.4	343.0	13.5	249.0	8.5	17.0	0.211	42.9	47.0	7.1	2.1	1.0
6	0	31	24.97	25.53	109.03	65.42	80.82	46.40	34.42	1.35	1.51	5.60	1.33	3.82	6.14	95.98	446.10	NaN	NaN	NaN	NaN	NaN	6.81	6.03	172.0	0.504	83.6	28.5	341.0	12.0	246.0	10.8	13.3	0.270	52.9	32.0	11.3	3.1	0.7
7	0	55	37.32	40.03	88.49	25.15	74.17	41.63	32.54	1.28	1.32	5.92	1.28	4.20	6.31	92.16	344.73	NaN	NaN	NaN	NaN	NaN	5.78	4.90	158.0	0.448	91.4	32.2	353.0	12.5	282.0	10.5	13.0	0.300	52.8	36.9	6.6	2.8	0.9
8	0	39	21.70	39.17	102.91	67.09	76.12	49.31	26.81	1.84	2.07	5.50	1.51	3.60	4.98	78.57	353.31	0.060	0.100	0.030	2.230	2.170	6.96	4.94	163.0	0.482	97.6	33.0	338.0	11.8	275.0	9.8	11.4	0.270	53.1	35.9	7.3	3.3	0.4
9	1	35	18.89	15.79	78.21	25.75	75.94	46.64	29.30	1.59	1.25	3.98	1.55	2.02	6.61	86.88	266.52	0.010	0.020	0.020	2.450	2.350	8.91	4.81	144.0	0.437	90.9	29.9	330.0	11.8	247.0	10.7	12.6	0.270	65.6	27.3	5.4	1.0	0.7
10	0	47	27.33	35.76	80.26	84.97	75.84	43.71	32.13	1.36	1.96	4.32	0.90	2.84	5.60	66.49	333.60	NaN	NaN	NaN	NaN	NaN	5.44	4.97	160.0	0.452	90.9	32.2	354.0	12.4	178.0	10.8	13.5	0.190	54.0	35.7	8.1	1.3	0.9
11	0	50	22.89	28.46	77.54	48.16	73.80	48.32	25.48	1.90	3.32	4.47	1.14	2.65	4.69	74.53	493.07	NaN	NaN	NaN	NaN	NaN	6.80	4.86	151.0	0.453	93.2	31.1	333.0	11.9	259.0	10.3	12.0	0.270	57.8	33.1	7.5	1.2	0.4
12	1	48	30.11	34.70	92.67	16.39	77.86	43.05	34.81	1.24	1.21	3.69	1.43	1.94	4.39	67.54	264.32	NaN	NaN	NaN	NaN	NaN	5.26	4.89	137.0	0.407	83.2	28.0	337.0	12.0	223.0	12.4	17.2	0.280	38.8	48.5	7.2	4.2	1.3
13	1	36	16.82	11.80	67.79	13.16	76.84	47.56	29.28	1.62	0.27	2.85	1.93	0.56	3.47	73.26	345.48	NaN	NaN	NaN	NaN	NaN	6.67	4.58	141.0	0.415	90.6	30.8	340.0	11.9	330.0	9.2	9.9	0.300	72.5	20.5	6.1	0.6	0.3
14	1	37	24.58	24.64	96.43	27.79	92.31	46.98	45.33	1.04	1.22	5.20	1.21	3.55	3.46	65.99	383.20	NaN	NaN	NaN	NaN	NaN	8.87	4.69	146.0	0.428	91.3	31.1	341.0	12.6	344.0	11.9	15.5	0.410	57.5	33.4	6.8	1.5	0.8
15	1	43	20.07	14.81	72.11	33.98	74.43	44.49	29.94	1.49	1.52	5.51	1.58	3.52	5.67	62.26	300.92	NaN	NaN	NaN	NaN	NaN	10.61	4.48	136.0	0.409	91.3	30.4	333.0	12.7	424.0	10.7	12.7	0.450	64.9	28.5	5.0	1.1	0.5
16	0	77	18.49	17.29	104.91	41.33	76.02	42.39	33.63	1.26	4.59	4.05	1.02	2.49	10.68	134.63	437.37	NaN	NaN	NaN	NaN	NaN	12.84	4.95	158.0	0.442	89.3	31.9	357.0	12.0	303.0	9.9	11.5	0.300	73.0	17.5	7.2	1.9	0.4
17	0	42	24.71	30.26	95.70	37.97	81.87	43.85	38.02	1.15	3.87	5.83	1.11	4.07	6.77	90.66	485.17	NaN	NaN	NaN	NaN	NaN	8.13	6.39	121.0	0.392	61.3	18.9	309.0	18.6	263.0	11.4	16.1	0.300	55.0	35.8	8.0	1.0	0.2
18	0	39	31.44	43.97	92.58	35.84	79.74	46.49	33.25	1.40	1.33	4.55	1.11	3.40	4.75	107.33	565.31	NaN	NaN	NaN	NaN	NaN	8.88	5.34	155.0	0.466	87.3	29.0	333.0	13.2	274.0	11.2	13.4	0.310	61.5	28.2	6.0	3.5	0.8
19	0	38	23.58	31.19	102.16	44.01	81.50	51.16	30.34	1.69	2.63	5.93	1.20	4.29	3.07	94.16	468.95	NaN	NaN	NaN	NaN	NaN	6.74	5.70	171.0	0.498	87.4	30.0	343.0	13.9	193.0	11.2	13.6	0.220	54.4	36.8	7.1	1.3	0.4
20	0	30	33.25	67.19	82.22	59.91	82.80	49.32	33.48	1.47	3.44	5.50	1.18	4.02	3.79	103.15	493.38	NaN	NaN	NaN	NaN	NaN	6.44	6.12	181.0	0.540	88.2	29.6	335.0	12.1	256.0	11.6	16.2	0.300	58.4	33.7	5.3	2.0	0.6
21	0	51	26.53	20.44	67.03	25.84	75.70	48.68	27.02	1.80	1.87	4.48	1.36	2.81	4.25	98.69	398.20	NaN	NaN	NaN	NaN	NaN	6.46	5.25	175.2	0.509	97.0	33.4	344.0	12.9	166.0	9.9	17.2	0.164	58.2	33.9	5.2	2.4	0.3
22	1	38	24.13	34.02	77.32	18.78	76.12	44.87	31.25	1.44	1.42	4.78	1.73	2.71	3.56	65.55	236.15	NaN	NaN	NaN	NaN	NaN	5.98	3.31	81.0	0.264	79.8	24.5	307.0	12.8	571.0	10.1	10.1	0.580	55.9	27.6	11.5	4.3	0.7
23	1	33	15.77	11.43	95.86	20.45	83.11	47.37	35.74	1.33	0.94	3.49	1.22	1.97	5.93	65.04	254.75	NaN	NaN	NaN	NaN	NaN	5.48	4.68	140.0	0.412	88.0	29.9	340.0	12.1	196.0	11.5	14.2	0.230	45.8	47.4	5.3	1.1	0.4
24	1	28	20.24	12.55	61.90	7.99	76.97	47.89	29.08	1.65	0.60	4.88	1.92	2.45	4.27	71.37	241.98	NaN	NaN	NaN	NaN	NaN	4.90	4.86	147.0	0.440	90.5	30.2	334.0	12.7	206.0	10.4	11.5	0.210	49.0	44.1	5.5	0.8	0.6
25	1	34	20.89	16.16	102.40	14.69	77.07	43.72	33.35	1.31	1.40	5.93	1.47	4.37	4.36	68.11	263.35	NaN	NaN	NaN	NaN	NaN	7.78	4.90	139.0	0.418	85.3	28.4	333.0	13.9	293.0	11.8	15.4	0.350	55.4	39.2	4.5	0.3	0.6
26	0	34	32.03	52.74	102.73	34.53	79.11	50.40	28.71	1.76	1.18	5.93	1.68	4.13	6.20	80.23	446.16	NaN	NaN	NaN	NaN	NaN	6.33	5.17	153.0	0.458	88.6	29.6	334.0	12.0	264.0	9.4	10.0	0.250	56.1	35.4	6.8	1.1	0.6
27	0	38	34.62	54.43	119.68	51.14	78.47	49.26	29.21	1.69	2.46	5.37	1.12	3.92	4.00	88.32	336.44	NaN	NaN	NaN	NaN	NaN	7.26	5.58	172.0	0.506	90.7	30.8	340.0	12.2	223.0	11.1	15.1	0.250	55.1	33.1	9.1	1.9	0.8
28	0	60	28.36	32.30	64.43	30.76	71.38	43.21	28.17	1.53	0.92	4.59	1.53	2.69	8.25	78.72	322.39	NaN	NaN	NaN	NaN	NaN	4.83	5.04	156.0	0.468	92.9	31.0	333.0	12.3	201.0	10.5	11.4	0.210	54.2	35.8	7.5	1.7	0.8
29	0	55	29.60	34.07	99.96	41.95	82.33	45.49	36.84	1.23	1.62	5.28	1.25	3.58	5.33	88.48	389.35	NaN	NaN	NaN	NaN	NaN	5.85	5.40	165.0	0.493	91.3	30.6	335.0	13.2	260.0	11.1	13.5	0.290	55.7	30.1	10.4	2.6	1.2
30	0	56	22.68	16.93	102.98	30.43	73.75	47.42	26.33	1.80	0.69	5.41	2.07	2.85	3.66	68.16	245.80	NaN	NaN	NaN	NaN	NaN	7.78	4.71	150.0	0.449	95.3	31.8	334.0	12.6	308.0	10.4	12.1	0.320	50.4	38.0	9.4	1.8	0.4
31	0	59	39.70	28.17	118.78	22.64	76.72	43.77	32.95	1.33	0.78	5.43	1.73	3.43	5.99	96.64	278.50	NaN	NaN	NaN	NaN	NaN	6.20	5.60	166.0	0.482	86.1	29.6	344.0	13.1	187.0	10.2	12.1	0.190	50.3	42.1	6.8	0.5	0.3
32	0	58	20.84	23.66	83.13	150.09	71.55	44.96	26.59	1.69	4.85	6.78	1.77	4.50	7.52	96.31	323.78	NaN	NaN	NaN	NaN	NaN	9.89	4.60	152.0	0.432	93.9	33.0	352.0	12.4	273.0	9.8	10.6	0.270	39.1	53.0	5.9	1.3	0.7
33	0	32	30.23	39.73	67.52	27.26	79.21	46.96	32.25	1.46	2.10	6.06	1.35	4.10	4.35	73.93	389.17	NaN	NaN	NaN	NaN	NaN	4.96	5.41	161.0	0.473	87.4	29.8	340.0	12.1	253.0	10.8	13.5	0.270	56.8	33.1	8.1	1.4	0.6
34	0	35	20.93	37.67	64.63	46.95	75.52	46.23	29.29	1.58	3.51	4.77	0.88	3.29	4.09	100.13	378.25	NaN	NaN	NaN	NaN	NaN	6.71	5.08	158.0	0.463	91.1	31.1	341.0	12.3	210.0	10.4	12.6	0.220	34.1	52.5	7.9	4.9	0.6
35	1	38	18.81	12.55	52.55	16.03	81.27	48.17	33.10	1.46	1.02	5.30	1.46	3.48	8.48	77.19	366.38	NaN	NaN	NaN	NaN	NaN	6.93	5.04	145.0	0.430	85.3	28.8	337.0	13.0	272.0	10.1	11.7	0.270	55.4	35.1	6.9	2.2	0.4
36	1	47	26.58	16.03	71.79	30.68	82.17	42.87	39.30	1.09	1.43	5.76	1.24	4.02	3.48	63.55	243.69	NaN	NaN	NaN	NaN	NaN	7.36	4.53	134.9	0.396	87.3	29.8	341.0	13.3	335.0	8.5	15.7	0.283	51.6	35.0	8.0	4.6	0.8
37	1	36	17.99	10.11	55.82	26.14	75.47	43.18	32.29	1.34	1.53	5.70	1.27	3.92	6.48	69.54	311.17	NaN	NaN	NaN	NaN	NaN	7.48	4.02	129.0	0.375	93.3	32.1	344.0	12.3	274.0	10.0	11.1	0.270	57.6	35.0	6.0	0.9	0.5
38	1	45	20.58	11.25	60.21	11.62	75.65	43.98	31.67	1.39	0.94	4.63	1.65	2.75	5.38	73.60	227.93	NaN	NaN	NaN	NaN	NaN	6.12	4.51	135.6	0.405	89.8	30.0	335.0	12.7	213.0	8.7	16.5	0.185	50.0	40.9	7.9	0.6	0.5
39	1	53	20.47	10.76	132.27	21.11	76.45	46.28	30.17	1.53	1.47	6.65	1.66	4.37	3.06	65.44	225.15	NaN	NaN	NaN	NaN	NaN	5.42	4.46	134.0	0.408	91.5	30.0	328.0	13.1	402.0	9.7	10.5	0.390	47.8	43.2	7.0	1.3	0.7

	GLU
0	1
1	0
2	0
3	0
4	0
5	1
6	0
7	1
8	1
9	0
10	0
11	0
12	0
13	0
14	0
15	0
16	1
17	0
18	1
19	0
20	0
21	0
22	0
23	0
24	0
25	0
26	1
27	1
28	0
29	0
30	0
31	1
32	0
33	0
34	0
35	0
36	0
37	1
38	0
39	1

	GLU
0	1.0
1	0.0
2	0.0
3	0.0
4	0.0
5	1.0
6	0.0
7	1.0
8	1.0
9	0.0
10	0.0
11	0.0
12	0.0
13	0.0
14	0.0
15	0.0
16	1.0
17	0.0
18	1.0
19	0.0
20	0.0
21	0.0
22	0.0
23	0.0
24	0.0
25	0.0
26	1.0
27	1.0
28	0.0
29	0.0
30	0.0
31	1.0
32	0.0
33	0.0
34	0.0
35	0.0
36	0.0
37	1.0
38	0.0
39	1.0

	sex	age	AST	ALT	ALP	GGT	TP	Alb	GLB	A/G	TG	TCHO	HDL-C	LDL-C	Urea	Cre	UA	HBsAg	Second liver antibody	HBeAg	HBeAb	HBcAb	WBC	RBC	HGB	PCV	MCV	MCH	MCHC	RDW	PLT	MPV	PDW	PCT	NEUT%	LY%	MONO%	EOS%	BAS%	GLU
0	0	41	24.960000	23.100000	99.590000	20.230000	76.880000	49.600000	27.280000	1.820000	1.310000	4.430000	1.370000	2.650000	5.870000	77.250000	349.390000	0.412369	6.624946	0.079442	1.805167	2.028279	5.340000	5.210	166.100000	0.4790	91.90	31.90	347.000000	12.800000	166.000000	9.900000	17.400000	0.1640	54.100000	34.200000	6.500000	4.700000	0.600000	1
1	0	41	24.570000	36.250000	67.210000	79.000000	79.430000	47.760000	31.670000	1.510000	2.810000	4.060000	0.930000	2.630000	5.260000	87.120000	486.780000	0.412369	6.624946	0.079442	1.805167	2.028279	7.650000	5.210	156.000000	0.4560	87.50	29.90	342.000000	13.400000	277.000000	9.200000	10.300000	0.2600	52.000000	36.700000	5.800000	4.700000	0.800000	0
2	0	46	20.820000	15.230000	63.690000	38.170000	86.230000	48.000000	38.230000	1.260000	0.990000	4.130000	1.640000	2.010000	4.770000	78.190000	452.070000	0.010000	0.020000	0.010000	1.370000	1.070000	4.600000	4.760	148.800000	0.4380	91.90	31.30	340.000000	13.000000	241.000000	8.300000	16.600000	0.1990	48.100000	40.300000	7.700000	3.200000	0.800000	0
3	1	22	14.990000	10.590000	74.080000	20.220000	70.980000	44.020000	26.960000	1.630000	1.060000	6.890000	1.430000	5.040000	4.230000	61.460000	368.850000	0.023333	0.196667	0.010000	1.320000	1.236667	9.360000	4.290	137.000000	0.4030	93.90	31.90	340.000000	12.600000	252.000000	10.300000	10.800000	0.2600	41.700000	46.500000	6.700000	4.600000	0.500000	0
4	1	48	20.070000	14.780000	75.790000	22.720000	78.050000	41.830000	36.220000	1.150000	0.970000	5.370000	1.270000	3.650000	5.170000	64.060000	376.360000	0.036667	0.373333	0.010000	1.270000	1.403333	5.070000	5.150	106.000000	0.3540	68.70	20.60	299.000000	16.600000	316.000000	11.100000	14.000000	0.3500	56.600000	33.100000	9.100000	0.600000	0.600000	0
5	1	74	23.720000	22.590000	81.230000	23.350000	76.460000	45.850000	30.610000	1.500000	2.450000	6.650000	1.810000	4.280000	6.110000	66.660000	383.870000	0.050000	0.550000	0.010000	1.220000	1.570000	7.000000	4.640	141.300000	0.4130	88.80	30.40	343.000000	13.500000	249.000000	8.500000	17.000000	0.2110	42.900000	47.000000	7.100000	2.100000	1.000000	1
6	0	31	24.970000	25.530000	109.030000	65.420000	80.820000	46.400000	34.420000	1.350000	1.510000	5.600000	1.330000	3.820000	6.140000	95.980000	446.100000	0.053333	0.400000	0.016667	1.556667	1.770000	6.810000	6.030	172.000000	0.5040	83.60	28.50	341.000000	12.000000	246.000000	10.800000	13.300000	0.2700	52.900000	32.000000	11.300000	3.100000	0.700000	0
7	0	55	37.320000	40.030000	88.490000	25.150000	74.170000	41.630000	32.540000	1.280000	1.320000	5.920000	1.280000	4.200000	6.310000	92.160000	344.730000	0.056667	0.250000	0.023333	1.893333	1.970000	5.780000	4.900	158.000000	0.4480	91.40	32.20	353.000000	12.500000	282.000000	10.500000	13.000000	0.3000	52.800000	36.900000	6.600000	2.800000	0.900000	1
8	0	39	21.700000	39.170000	102.910000	67.090000	76.120000	49.310000	26.810000	1.840000	2.070000	5.500000	1.510000	3.600000	4.980000	78.570000	353.310000	0.060000	0.100000	0.030000	2.230000	2.170000	6.960000	4.940	163.000000	0.4820	97.60	33.00	338.000000	11.800000	275.000000	9.800000	11.400000	0.2700	53.100000	35.900000	7.300000	3.300000	0.400000	1
9	1	35	18.890000	15.790000	78.210000	25.750000	75.940000	46.640000	29.300000	1.590000	1.250000	3.980000	1.550000	2.020000	6.610000	86.880000	266.520000	0.010000	0.020000	0.020000	2.450000	2.350000	8.910000	4.810	144.000000	0.4370	90.90	29.90	330.000000	11.800000	247.000000	10.700000	12.600000	0.2700	65.600000	27.300000	5.400000	1.000000	0.700000	0
10	0	47	27.330000	35.760000	80.260000	84.970000	75.840000	43.710000	32.130000	1.360000	1.960000	4.320000	0.900000	2.840000	5.600000	66.490000	333.600000	0.010321	0.020641	0.020256	2.443333	2.346346	5.440000	4.970	160.000000	0.4520	90.90	32.20	354.000000	12.400000	178.000000	10.800000	13.500000	0.1900	54.000000	35.700000	8.100000	1.300000	0.900000	0
11	0	50	22.890000	28.460000	77.540000	48.160000	73.800000	48.320000	25.480000	1.900000	3.320000	4.470000	1.140000	2.650000	4.690000	74.530000	493.070000	0.010641	0.021282	0.020513	2.436667	2.342692	6.800000	4.860	151.000000	0.4530	93.20	31.10	333.000000	11.900000	259.000000	10.300000	12.000000	0.2700	57.800000	33.100000	7.500000	1.200000	0.400000	0
12	1	48	30.110000	34.700000	92.670000	16.390000	77.860000	43.050000	34.810000	1.240000	1.210000	3.690000	1.430000	1.940000	4.390000	67.540000	264.320000	0.010962	0.021923	0.020769	2.430000	2.339038	5.260000	4.890	137.000000	0.4070	83.20	28.00	337.000000	12.000000	223.000000	12.400000	17.200000	0.2800	38.800000	48.500000	7.200000	4.200000	1.300000	0
13	1	36	16.820000	11.800000	67.790000	13.160000	76.840000	47.560000	29.280000	1.620000	0.270000	2.850000	1.930000	0.560000	3.470000	73.260000	345.480000	0.011282	0.022564	0.021026	2.423333	2.335385	6.670000	4.580	141.000000	0.4150	90.60	30.80	340.000000	11.900000	330.000000	9.200000	9.900000	0.3000	72.500000	20.500000	6.100000	0.600000	0.300000	0
14	1	37	24.580000	24.640000	96.430000	27.790000	92.310000	46.980000	45.330000	1.040000	1.220000	5.200000	1.210000	3.550000	3.460000	65.990000	383.200000	0.011603	0.023205	0.021282	2.416667	2.331731	8.870000	4.690	146.000000	0.4280	91.30	31.10	341.000000	12.600000	344.000000	11.900000	15.500000	0.4100	57.500000	33.400000	6.800000	1.500000	0.800000	0
15	1	43	20.070000	14.810000	72.110000	33.980000	74.430000	44.490000	29.940000	1.490000	1.520000	5.510000	1.580000	3.520000	5.670000	62.260000	300.920000	0.011923	0.023846	0.021538	2.410000	2.328077	10.610000	4.480	136.000000	0.4090	91.30	30.40	333.000000	12.700000	424.000000	10.700000	12.700000	0.4500	64.900000	28.500000	5.000000	1.100000	0.500000	0
16	0	77	18.490000	17.290000	104.910000	41.330000	76.020000	42.390000	33.630000	1.260000	4.590000	4.050000	1.020000	2.490000	10.680000	134.630000	437.370000	0.012244	0.024487	0.021795	2.403333	2.324423	12.840000	4.950	158.000000	0.4420	89.30	31.90	357.000000	12.000000	303.000000	9.900000	11.500000	0.3000	73.000000	17.500000	7.200000	1.900000	0.400000	1
17	0	42	24.710000	30.260000	95.700000	37.970000	81.870000	43.850000	38.020000	1.150000	3.870000	5.830000	1.110000	4.070000	6.770000	90.660000	485.170000	0.012564	0.025128	0.022051	2.396667	2.320769	8.130000	6.390	121.000000	0.3920	61.30	18.90	309.000000	18.600000	263.000000	11.400000	16.100000	0.3000	55.000000	35.800000	8.000000	1.000000	0.200000	0
18	0	39	31.440000	43.970000	92.580000	35.840000	79.740000	46.490000	33.250000	1.400000	1.330000	4.550000	1.110000	3.400000	4.750000	107.330000	565.310000	0.012885	0.025769	0.022308	2.390000	2.317115	8.880000	5.340	155.000000	0.4660	87.30	29.00	333.000000	13.200000	274.000000	11.200000	13.400000	0.3100	61.500000	28.200000	6.000000	3.500000	0.800000	1
19	0	38	23.580000	31.190000	102.160000	44.010000	81.500000	51.160000	30.340000	1.690000	2.630000	5.930000	1.200000	4.290000	3.070000	94.160000	468.950000	0.013205	0.026410	0.022564	2.383333	2.313462	6.740000	5.700	171.000000	0.4980	87.40	30.00	343.000000	13.900000	193.000000	11.200000	13.600000	0.2200	54.400000	36.800000	7.100000	1.300000	0.400000	0
20	0	30	33.250000	67.190000	82.220000	59.910000	82.800000	49.320000	33.480000	1.470000	3.440000	5.500000	1.180000	4.020000	3.790000	103.150000	493.380000	0.013526	0.027051	0.022821	2.376667	2.309808	6.440000	6.120	181.000000	0.5400	88.20	29.60	335.000000	12.100000	256.000000	11.600000	16.200000	0.3000	58.400000	33.700000	5.300000	2.000000	0.600000	0
21	0	51	26.530000	20.440000	67.030000	25.840000	75.700000	48.680000	27.020000	1.800000	1.870000	4.480000	1.360000	2.810000	4.250000	98.690000	398.200000	0.013846	0.027692	0.023077	2.370000	2.306154	6.460000	5.250	175.200000	0.5090	97.00	33.40	344.000000	12.900000	166.000000	9.900000	17.200000	0.1640	58.200000	33.900000	5.200000	2.400000	0.300000	0
22	1	38	24.130000	34.020000	77.320000	18.780000	76.120000	44.870000	31.250000	1.440000	1.420000	4.780000	1.730000	2.710000	3.560000	65.550000	236.150000	0.014167	0.028333	0.023333	2.363333	2.302500	5.980000	3.310	81.000000	0.2640	79.80	24.50	307.000000	12.800000	571.000000	10.100000	10.100000	0.5800	55.900000	27.600000	11.500000	4.300000	0.700000	0
23	1	33	15.770000	11.430000	95.860000	20.450000	83.110000	47.370000	35.740000	1.330000	0.940000	3.490000	1.220000	1.970000	5.930000	65.040000	254.750000	0.014487	0.028974	0.023590	2.356667	2.298846	5.480000	4.680	140.000000	0.4120	88.00	29.90	340.000000	12.100000	196.000000	11.500000	14.200000	0.2300	45.800000	47.400000	5.300000	1.100000	0.400000	0
24	1	28	20.240000	12.550000	61.900000	7.990000	76.970000	47.890000	29.080000	1.650000	0.600000	4.880000	1.920000	2.450000	4.270000	71.370000	241.980000	0.014808	0.029615	0.023846	2.350000	2.295192	4.900000	4.860	147.000000	0.4400	90.50	30.20	334.000000	12.700000	206.000000	10.400000	11.500000	0.2100	49.000000	44.100000	5.500000	0.800000	0.600000	0
25	1	34	20.890000	16.160000	102.400000	14.690000	77.070000	43.720000	33.350000	1.310000	1.400000	5.930000	1.470000	4.370000	4.360000	68.110000	263.350000	0.015128	0.030256	0.024103	2.343333	2.291538	7.780000	4.900	139.000000	0.4180	85.30	28.40	333.000000	13.900000	293.000000	11.800000	15.400000	0.3500	55.400000	39.200000	4.500000	0.300000	0.600000	0
26	0	34	32.030000	52.740000	102.730000	34.530000	79.110000	50.400000	28.710000	1.760000	1.180000	5.930000	1.680000	4.130000	6.200000	80.230000	446.160000	0.015449	0.030897	0.024359	2.336667	2.287885	6.330000	5.170	153.000000	0.4580	88.60	29.60	334.000000	12.000000	264.000000	9.400000	10.000000	0.2500	56.100000	35.400000	6.800000	1.100000	0.600000	1
27	0	38	34.620000	54.430000	119.680000	51.140000	78.470000	49.260000	29.210000	1.690000	2.460000	5.370000	1.120000	3.920000	4.000000	88.320000	336.440000	0.015769	0.031538	0.024615	2.330000	2.284231	7.260000	5.580	172.000000	0.5060	90.70	30.80	340.000000	12.200000	223.000000	11.100000	15.100000	0.2500	55.100000	33.100000	9.100000	1.900000	0.800000	1
28	0	60	28.360000	32.300000	64.430000	30.760000	71.380000	43.210000	28.170000	1.530000	0.920000	4.590000	1.530000	2.690000	8.250000	78.720000	322.390000	0.016090	0.032179	0.024872	2.323333	2.280577	4.830000	5.040	156.000000	0.4680	92.90	31.00	333.000000	12.300000	201.000000	10.500000	11.400000	0.2100	54.200000	35.800000	7.500000	1.700000	0.800000	0
29	0	55	29.600000	34.070000	99.960000	41.950000	82.330000	45.490000	36.840000	1.230000	1.620000	5.280000	1.250000	3.580000	5.330000	88.480000	389.350000	0.016410	0.032821	0.025128	2.316667	2.276923	5.850000	5.400	165.000000	0.4930	91.30	30.60	335.000000	13.200000	260.000000	11.100000	13.500000	0.2900	55.700000	30.100000	10.400000	2.600000	1.200000	0
30	0	56	22.680000	16.930000	102.980000	30.430000	73.750000	47.420000	26.330000	1.800000	0.690000	5.410000	2.070000	2.850000	3.660000	68.160000	245.800000	0.016731	0.033462	0.025385	2.310000	2.273269	7.780000	4.710	150.000000	0.4490	95.30	31.80	334.000000	12.600000	308.000000	10.400000	12.100000	0.3200	50.400000	38.000000	9.400000	1.800000	0.400000	0
31	0	59	39.700000	28.170000	118.780000	22.640000	76.720000	43.770000	32.950000	1.330000	0.780000	5.430000	1.730000	3.430000	5.990000	96.640000	278.500000	0.017051	0.034103	0.025641	2.303333	2.269615	6.200000	5.600	166.000000	0.4820	86.10	29.60	344.000000	13.100000	187.000000	10.200000	12.100000	0.1900	50.300000	42.100000	6.800000	0.500000	0.300000	1
32	0	58	20.840000	23.660000	83.130000	150.090000	71.550000	44.960000	26.590000	1.690000	4.850000	6.780000	1.770000	4.500000	7.520000	96.310000	323.780000	0.017372	0.034744	0.025897	2.296667	2.265962	9.890000	4.600	152.000000	0.4320	93.90	33.00	352.000000	12.400000	273.000000	9.800000	10.600000	0.2700	39.100000	53.000000	5.900000	1.300000	0.700000	0
33	0	32	30.230000	39.730000	67.520000	27.260000	79.210000	46.960000	32.250000	1.460000	2.100000	6.060000	1.350000	4.100000	4.350000	73.930000	389.170000	0.017692	0.035385	0.026154	2.290000	2.262308	4.960000	5.410	161.000000	0.4730	87.40	29.80	340.000000	12.100000	253.000000	10.800000	13.500000	0.2700	56.800000	33.100000	8.100000	1.400000	0.600000	0
34	0	35	20.930000	37.670000	64.630000	46.950000	75.520000	46.230000	29.290000	1.580000	3.510000	4.770000	0.880000	3.290000	4.090000	100.130000	378.250000	0.018013	0.036026	0.026410	2.283333	2.258654	6.710000	5.080	158.000000	0.4630	91.10	31.10	341.000000	12.300000	210.000000	10.400000	12.600000	0.2200	34.100000	52.500000	7.900000	4.900000	0.600000	0
35	1	38	18.810000	12.550000	52.550000	16.030000	81.270000	48.170000	33.100000	1.460000	1.020000	5.300000	1.460000	3.480000	8.480000	77.190000	366.380000	0.018333	0.036667	0.026667	2.276667	2.255000	6.930000	5.040	145.000000	0.4300	85.30	28.80	337.000000	13.000000	272.000000	10.100000	11.700000	0.2700	55.400000	35.100000	6.900000	2.200000	0.400000	0
36	1	47	26.580000	16.030000	71.790000	30.680000	82.170000	42.870000	39.300000	1.090000	1.430000	5.760000	1.240000	4.020000	3.480000	63.550000	243.690000	0.018654	0.037308	0.026923	2.270000	2.251346	7.360000	4.530	134.900000	0.3960	87.30	29.80	341.000000	13.300000	335.000000	8.500000	15.700000	0.2830	51.600000	35.000000	8.000000	4.600000	0.800000	0
37	1	36	17.990000	10.110000	55.820000	26.140000	75.470000	43.180000	32.290000	1.340000	1.530000	5.700000	1.270000	3.920000	6.480000	69.540000	311.170000	0.018974	0.037949	0.027179	2.263333	2.247692	7.480000	4.020	129.000000	0.3750	93.30	32.10	344.000000	12.300000	274.000000	10.000000	11.100000	0.2700	57.600000	35.000000	6.000000	0.900000	0.500000	1
38	1	45	20.580000	11.250000	60.210000	11.620000	75.650000	43.980000	31.670000	1.390000	0.940000	4.630000	1.650000	2.750000	5.380000	73.600000	227.930000	0.019295	0.038590	0.027436	2.256667	2.244038	6.120000	4.510	135.600000	0.4050	89.80	30.00	335.000000	12.700000	213.000000	8.700000	16.500000	0.1850	50.000000	40.900000	7.900000	0.600000	0.500000	0
39	1	53	20.470000	10.760000	132.270000	21.110000	76.450000	46.280000	30.170000	1.530000	1.470000	6.650000	1.660000	4.370000	3.060000	65.440000	225.150000	0.019615	0.039231	0.027692	2.250000	2.240385	5.420000	4.460	134.000000	0.4080	91.50	30.00	328.000000	13.100000	402.000000	9.700000	10.500000	0.3900	47.800000	43.200000	7.000000	1.300000	0.700000	1

	GLU
0	0
1	0
2	0
3	1
4	1
5	0
6	0
7	1
8	0
9	1
10	0
11	0
12	0
13	0
14	1
15	1
16	0
17	1
18	1
19	1
20	0
21	1
22	1
23	1
24	0
25	0
26	0
27	1
28	1
29	0
30	0
31	0
32	0
33	1
34	0
35	1
36	1
37	0
38	0
39	0
40	0
41	1
42	0
43	1
44	0
45	1
46	1
47	0
48	0
49	1
50	0
51	1
52	0
53	0
54	0
55	0
56	0
57	0
58	0
59	0
60	0
61	0
62	1
63	0
64	0
65	0
66	0
67	0
68	0
69	0
70	1
71	1
72	0
73	0
74	0
75	0
76	0
77	0
78	1
79	1
80	1
81	0
82	1
83	0
84	0
85	0
86	0
87	0
88	1
89	1
90	0
91	1
92	1
93	1
94	0
95	0
96	1
97	1
98	1
99	1
100	0
101	1
102	1
103	1
104	1
105	0
106	0
107	0
108	0
109	1
110	1
111	0
112	1
113	1
114	0
115	1
116	0
117	0
118	1
119	0
120	1
121	0
122	0
123	0
124	0
125	1
126	0
127	0
128	0
129	0
130	0
131	0
132	0
133	1
134	0
135	0
136	0
137	1
138	1
139	1
140	0
141	0
142	1
143	1
144	0
145	0
146	1
147	0
148	1
149	0
150	1
151	0
152	0
153	0
154	0
155	0
156	0
157	1
158	1
159	0
160	0
161	1
162	1
163	0
164	1
165	1
166	0
167	0
168	0
169	0
170	1
171	0
172	0
173	1
174	0
175	1
176	0
177	0
178	0
179	0
180	1
181	0
182	0
183	0
184	0
185	0
186	0
187	1
188	0
189	0
190	0
191	0
192	1
193	0
194	0
195	0
196	0
197	1
198	1
199	1
200	0
201	1
202	0
203	1
204	0
205	0
206	1
207	1
208	1
209	0
210	0
211	0
212	0
213	0
214	1
215	0
216	1
217	1
218	1
219	0
220	1
221	0
222	0
223	0
224	0
225	1
226	0
227	1
228	1
229	0
230	0
231	0
232	0
233	1
234	0
235	1
236	0
237	1
238	0
239	0
240	1
241	1
242	0
243	1
244	1
245	1
246	1
247	1
248	0
249	1
250	1
251	0
252	1
253	1
254	1
255	0
256	0
257	1
258	1
259	0
260	1
261	0
262	1
263	0
264	0
265	1
266	1
267	1
268	1
269	0
270	0
271	1
272	1
273	0
274	0
275	1
276	1
277	0
278	0
279	0
280	0
281	1
282	1
283	1
284	1
285	0
286	1
287	0
288	0
289	0
290	1
291	1
292	1
293	1
294	1
295	0
296	1
297	0
298	1
299	1
300	1
301	0
302	1
303	1
304	0
305	0
306	0
307	0
308	1
309	0
310	1
311	0
312	0
313	1
314	0
315	1
316	1
317	1
318	1
319	0
320	0
321	0
322	0
323	0
324	0
325	1
326	0
327	1
328	0
329	0
330	0
331	0
332	1
333	0
334	0
335	1
336	1
337	1
338	0
339	1
340	0
341	1
342	1
343	0
344	1
345	0
346	0
347	0
348	0
349	0
350	0
351	0
352	0
353	1
354	0
355	0
356	1
357	0
358	1
359	1
360	0
361	1
362	0
363	0
364	0
365	0
366	1
367	0
368	1
369	0
370	1
371	0
372	1
373	0
374	0
375	0
376	1
377	0
378	1
379	0
380	0
381	1
382	0
383	1
384	1
385	0
386	1
387	1
388	0
389	0
390	0
391	1
392	1
393	0
394	0
395	1
396	1
397	1
398	1
399	0
400	0
401	0
402	0
403	1
404	0
405	0
406	0
407	0
408	1
409	0
410	1
411	0
412	0
413	1
414	0
415	1
416	0
417	0
418	0
419	1
420	1
421	1
422	1
423	0
424	0
425	0
426	0
427	0
428	0
429	1
430	0
431	1
432	0
433	0
434	0
435	1
436	1
437	0
438	0
439	0
440	1
441	0
442	1
443	0
444	0
445	0
446	1
447	0
448	0
449	0
450	0
451	1
452	1
453	0
454	1
455	1
456	1
457	0
458	1
459	1
460	1
461	0
462	0
463	0
464	1
465	0
466	1
467	0
468	0
469	0
470	0
471	0
472	0
473	0
474	0
475	0
476	1
477	0
478	0
479	0
480	1
481	0
482	1
483	0
484	0
485	1
486	0
487	0
488	0
489	0
490	0
491	0
492	0
493	0
494	1
495	0
496	0
497	0
498	0
499	0
500	0
501	0
502	0
503	1
504	0
505	1
506	0
507	0
508	0
509	0
510	1
511	1
512	0
513	0
514	0
515	0
516	0
517	1
518	0
519	0
520	0
521	0
522	1
523	1
524	1
525	0
526	0
527	0
528	0
529	0
530	0
531	0
532	0
533	0
534	1
535	0
536	0
537	0
538	0
539	0
540	0
541	0
542	1
543	0
544	1
545	1
546	0
547	1
548	0
549	1
550	1
551	0
552	0
553	0
554	0
555	0
556	1
557	0
558	1
559	0
560	1
561	1
562	0
563	0
564	1
565	1
566	0
567	1
568	1
569	1
570	0
571	1
572	0
573	0
574	1
575	0
576	1
577	0
578	0
579	1
580	0
581	0
582	1
583	0
584	1
585	0
586	0
587	1
588	1
589	1
590	0
591	0
592	0
593	1
594	1
595	0
596	0
597	1
598	1
599	1
600	1
601	1
602	1
603	0
604	0
605	1
606	0
607	1
608	1
609	1
610	0
611	0
612	1
613	1
614	1
615	1
616	0
617	0
618	0
619	1
620	0
621	0
622	0
623	0
624	0
625	0
626	0
627	0
628	1
629	1
630	0
631	0
632	0
633	0
634	1
635	1
636	1
637	1
638	0
639	1
640	1
641	1
642	0
643	1
644	0
645	0
646	0
647	1
648	1
649	0
650	0
651	0
652	0
653	0
654	1
655	0
656	0
657	0
658	1
659	0
660	0
661	0
662	0
663	0
664	0
665	0
666	0
667	1
668	1
669	1
670	0
671	0
672	1
673	0
674	0
675	0
676	0
677	0
678	0
679	0
680	1
681	0
682	0
683	1
684	1
685	1
686	0
687	0
688	0
689	0
690	1
691	1
692	0
693	0
694	1
695	0
696	0
697	0
698	1
699	1
700	0
701	0
702	0
703	0
704	0
705	0
706	0
707	1
708	0
709	1
710	0
711	0
712	0
713	1
714	1
715	0
716	0
717	0
718	0
719	0
720	0
721	1
722	1
723	0
724	1
725	0
726	1
727	0
728	0
729	0
730	0
731	0
732	1
733	0
734	0
735	0
736	0
737	0
738	0
739	0
740	1
741	0
742	0
743	0
744	0
745	1
746	0
747	1
748	1
749	1
750	1
751	1
752	0
753	0
754	1
755	0
756	1
757	0
758	0
759	0
760	0
761	0
762	0
763	1
764	0
765	0
766	0
767	0
768	0
769	1
770	0
771	0
772	0
773	1
774	0
775	0
776	0
777	1
778	1
779	0
780	0
781	0
782	1
783	0
784	0
785	0
786	0
787	0
788	1
789	1
790	0
791	1
792	0
793	1
794	0
795	0
796	0
797	1
798	0
799	0
800	0
801	0
802	0
803	0
804	0
805	0
806	1
807	0
808	0
809	0
810	0
811	0
812	0
813	1
814	1
815	0
816	1
817	0
818	1
819	0
820	0
821	1
822	1
823	1
824	0
825	1
826	0
827	1
828	0
829	1
830	1
831	0
832	1
833	0
834	0
835	1
836	0
837	0
838	0
839	1
840	0
841	1
842	0
843	0
844	1
845	1
846	1
847	0
848	0
849	1
850	1
851	0
852	0
853	1
854	0
855	1
856	0
857	0
858	0
859	1
860	0
861	0
862	1
863	1
864	0
865	0
866	0
867	0
868	1
869	0
870	1
871	1
872	0
873	0
874	0
875	0
876	0
877	0
878	0
879	1
880	0
881	1
882	1
883	0
884	0
885	1
886	0
887	1
888	0
889	0
890	0
891	0
892	1
893	0
894	0
895	0
896	0
897	1
898	0
899	1
900	1
901	0
902	0
903	0
904	1
905	0
906	1
907	0
908	0
909	0
910	0
911	0
912	0
913	1
914	1
915	1
916	1
917	0
918	0
919	0
920	0
921	1
922	0
923	0
924	1
925	1
926	0
927	0
928	1
929	1
930	1
931	1
932	0
933	1
934	1
935	0
936	1
937	0
938	1
939	0
940	0
941	0
942	0
943	1
944	0
945	1
946	0
947	0
948	0
949	0
950	0
951	1
952	1
953	0
954	1
955	0
956	0
957	0
958	0
959	1
960	0
961	0
962	0
963	0
964	0
965	0
966	0
967	0
968	0
969	0
970	1
971	0
972	0
973	1
974	0
975	0
976	0
977	0
978	0
979	0
980	1
981	0
982	0
983	1
984	0
985	1
986	0
987	1
988	1
989	0
990	0
991	0
992	0
993	1
994	1
995	0
996	0
997	0
998	0
999	1

	GLU
0	0
1	0
2	0
3	1
4	1
5	0
6	0
7	1
8	0
9	1
10	0
11	0
12	0
13	0
14	1
15	1
16	0
17	1
18	1
19	1
20	0
21	1
22	1
23	1
24	0
25	0
26	0
27	1
28	1
29	0
30	0
31	0
32	0
33	1
34	0
35	1
36	1
37	0
38	0
39	0
40	0
41	1
42	0
43	1
44	0
45	1
46	1
47	0
48	0
49	1
50	0
51	1
52	0
53	0
54	0
55	0
56	0
57	0
58	0
59	0
60	0
61	0
62	1
63	0
64	0
65	0
66	0
67	0
68	0
69	0
70	1
71	1
72	0
73	0
74	0
75	0
76	0
77	0
78	1
79	1
80	1
81	0
82	1
83	0
84	0
85	0
86	0
87	0
88	1
89	1
90	0
91	1
92	1
93	1
94	0
95	0
96	1
97	1
98	1
99	1
100	0
101	1
102	1
103	1
104	1
105	0
106	0
107	0
108	0
109	1
110	1
111	0
112	1
113	1
114	0
115	1
116	0
117	0
118	1
119	0
120	1
121	0
122	0
123	0
124	0
125	1
126	0
127	0
128	0
129	0
130	0
131	0
132	0
133	1
134	0
135	0
136	0
137	1
138	1
139	1
140	0
141	0
142	1
143	1
144	0
145	0
146	1
147	0
148	1
149	0
150	1
151	0
152	0
153	0
154	0
155	0
156	0
157	1
158	1
159	0
160	0
161	1
162	1
163	0
164	1
165	1
166	0
167	0
168	0
169	0
170	1
171	0
172	0
173	1
174	0
175	1
176	0
177	0
178	0
179	0
180	1
181	0
182	0
183	0
184	0
185	0
186	0
187	1
188	0
189	0
190	0
191	0
192	1
193	0
194	0
195	0
196	0
197	1
198	1
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